To properly understand the global water cycle, improve analysis of climate variability, verify climate models and assist in local decision-making of surface or air transport, it is necessary to have better field tools for measurement of snow. Previous work has developed models of the Geonor precipitation guage and has included k-epsilon based numerical models of the flow around shielded gauges. To improve these results, it is essential to pursue advanced turbulence models as well as to develop benchmark experimental results. Large-Eddy Simulation, or LES, has become the method of choice for computationally-intensive simulations resolved to the necessary scales. Traditional Reynolds-averaged methods (RANS), although useful, require significant assumptions that compromise the fidelity of the flow physics obtained.

Direct Numerical Simulation (DNS) which resolves all the scales remains a prohibitive method due to its computational requirements. LES bridges between RANS and DNS, where the energetic large scales are resolved and computed directly whereas the smaller more universal scales are modeled. By modeling the subgrid scales within the inertial subrange, it is possible to extract high-fidelity flow information that can be used to improve local conditions. However, LES is computationally intensive and micro-climate modeling is beyond the capability of most desktop computers. As well, until recently LES models were either lab-developed codes or commercial codes. Lab-developed codes are difficult to transfer to industry partners as they are not necessarily client-friendly. While there exists excellent commercial codes, using these on multi-processor machines is prohibitor expensive. To model this flow, OpenFoam, an open-source turbulence code, will be used. The use of LES, instead of RANS modeling, would allow improved physical modeling of snow precipitate and ensure better comparison to real flow.

Industry Partner(s):Novus Environmental

Academic Institution:University of Toronto

Academic Researcher: Pierre Sullivan

Focus Areas: Advanced Manufacturing, Cities, Water

Platforms: Parallel CPU